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Market Research and Insights

Marketing Research and Insights

Marketing Research and Insights 

Data enhanced marketing research enables you to use multiple, disparate sources of data to give you both broader and more in-depth insights into the customer and third party data you use. When data lacks context, your data loses value. Research allows you to act on your data in ways that drive more engagement, improve marketing relevance, and acquire more customers.

Primary Research and Appended Data 

Primary research is thoughtfully-constructed research specifically tailored for your marketing campaigns to increase their effectiveness. We find data to supplement — or append/augment — what is available so you can predict not only how a person would behave, but what motivates those behaviors. From there, you can create more powerful messaging. Your marketing campaigns are more effective because the messaging is meaningful to your intended audience. It’s all about relevance.

We combine data gathered from a consumer’s digital footprint (i.e. website or social media engagement), offline activity (call centers), and information gathered through custom surveys.

Online or offline survey results Online or offline survey results
Insight communities Insight communities
Point-of-Scale (PoS) terminals Point-of-Scale (PoS) terminals
Email interaction Email interaction
Website engagement and e-commerce Website engagement and e-commerce
Call center interactions Call center interactions
Segmentation and market strategy

Segmentation and Market Strategy

Marketing campaigns fall flat if they’re not directed to the right individuals. Consumer segmentation (attitudinal, behavioral, or needs based) is the first step towards determining which target audiences will make the most sense to achieve your goals.  

As you segment your target audience, you also need a clear understanding of your market, competitors, and emerging trends. From here, you can begin formulating a strategy.

Why Data Segmentation?

  • Personalize messages for individuals that share unique traits, behavioral characteristics, opinions, or needs.
  • Recommend products or services to individuals that have demonstrated interest in similar products or services
  • Determine which data you do and do not need for a campaign
  • Create customized email and/or direct mailing lists for recurring messages (i.e. loyalty campaign)

Product Innovation

You might be designing a new product, or maybe you’re thinking about releasing new products to market in the coming years. Whatever the case, you need to know what makes your customers tick and their propensity to buy a certain product. In short, you need to know their needs and wants. What we research:
  • Product awareness and usage (A&U) information

  • Customer product satisfaction

  • Optimization of product configuration alternatives

  • Product pricing and marketing information

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Analytics and Data Modeling 

When you can accurately derive trends from your data or predict how a segment of people will react to specific messaging and products, you have incredible power at your fingertips. Use this power to build campaigns and speak to people rather than at people.

Here are just a few ways data models can be applied:

Customer's Lifetime Value Icon
Determine a customer's lifetime value through recency, frequency, and monetary analysis.
Purchase Propensity Icon
Learn customers' purchase propensity within the category and for your brand.
Churn Risk Icon
Learn customer's churn risk and how to mitigate that risk.
Customer Profile Icon
Create complete customer profiles that include behavioral and attitudinal information.
Identity Resolution Icon
Identity resolution, which helps find the right individuals for digital marketing campaigns.
Marketing Response Icon
Marketing response analysis predicts a group's response without resorting to expensive in-market campaign testing.

View Our List of Models

Adaptive Choice-Based Conjoint (ACBC)

An advanced form of conjoint analysis used for highly complex product configurations and/or extensive feature-driven pricing.

Choice Based Conjoint (CBC)

Measures the importance of different product attributes on consumers’ choices (i.e the appeal of color vs. box size) and the ideal level of those attributes (blue might outperform green).

Apriori Association Rules

Also known as basket analysis, this measures which products consumers buy together, either in the same basket or sequentially.


Also known as “decision trees,” CHAID/CRT is a form of descriptive modeling that demonstrates the effects of different values’ contributions to some outcome.  For example, an outcome such as the response to an offer might “split” based on different levels of household income, presence of children in the home, or state of residence.

Exploratory Data Analysis (EDA)

A comprehensive, quantitative analysis of existing data (existing information or variables that have been appended), often cross-tabulated by different groups of consumers (e.g. responders versus non-responders).

Gap Analysis

Frequently used to determine the gap between awareness of your brand and usage of your brand.  Small gaps between these two metrics imply a high level of acceptance or interest in your product, which warrants additional consumer outreach. Large gaps point to a different problem, and we deploy techniques to determine why those gaps exists.

Key Drivers Analysis

A “derived importance” model that quantifies those brand characteristics that determine brand loyalty or overall satisfaction in terms of 1) how satisfied consumers are with certain features of your brand, and 2) how important those features are.

Logistic Regression

Also referred to as response modeling, which is an analytical application used to determine which attributes are predictive of some outcome.  This technique then enables you to apply a probability, or propensity, to other consumers whose values on those attributes are known.

Machine Learning

A series of different computationally-intensive techniques used to predict outcomes often based on hundreds or thousands of variables and millions of rows of data.

Artificial Intelligence (AI)

The application of the machine learning algorithms to real-life conditions, often in real time. Typically, AI applications are somewhat different from other modeling tools because they are able to “learn” and improve their accuracy over time.

MaxDiff Scaling

A discrete choice research technique that is designed to yield the “maximum difference” between a series of unique alternatives.

Text Mining

Scrapes and subsequently converts raw, unstructured text data from social media and other sources into formats which enables that data to be quantified. These analyses are often word clouds, statistical distributions, hierarchical cluster analysis, and more.

TURF Analysis

Total unduplicated reach and frequency, which shows the combination of products which yield the highest number of unique consumers and/or the most frequency of purchase.
Business Intelligence

Business Intelligence

Your business performance is only as good as your ability to access your data, understand your data, and act on your data. Our business intelligence solution allows you to react in real time to trends and update campaigns accordingly.

Here’s what you can expect from our business intelligence offering:

  • Optimize your acquisition campaigns
  • Fix customer churn problems
  • Refine direct response offers
  • Customize regional marketing strategies
  • Improve cross-selling campaigns

"Do You Even Data" Blog

We're showcasing how marketers are leveraging data for performance as well as the insights marketers can draw from their data if they know where to look. If a data-driven marketing approach matters to you, let this be your guiding light.

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Your marketing is only as strong as the data you use. 


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